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Course Schedule

Lectures

Sep. 15, Tue
Machine Learning Recap and Perceptrons (Slides)
Chen Sun
  1. Recommended Reading: What is AI?
Sep. 17, Thu
Loss Functions and Optimization (Slides)
Chen Sun
Sep. 17, Thu
HW1 Math and Machine Learning Recap
  1. Handout
Sep. 22, Tue
Stochastic Gradient Descent and Multi-layer Perceptrons (Slides)
Chen Sun
Sep. 24, Thu
Backpropagation (Slides)
Chen Sun
  1. Recommended Reading: Hacker’s guide to Neural Networks
Sep. 29, Tue
Deep Learning Softwares and Hardwares
Chen Sun
Sep. 29, Tue
Final Final project teaming
Oct. 1, Thu
Convolutional Neural Network: Introduction
Chen Sun
  1. Recommended Reading: The Bitter Lesson
  2. Recommended Reading: Induction, Inductive Biases, and Infusing Knowledge into Learned Representations
Oct. 1, Thu
HW2 CNN
Oct. 6, Tue
Convolutional Neural Network: Architectures
Chen Sun
Oct. 8, Thu
Convolutional Neural Networks in Practice
Chen Sun
Oct. 13, Tue
Automatic Differentiation
Chen Sun
Oct. 15, Thu
Word Embeddings and Recurrent Neural Networks
Chen Sun
Oct. 15, Thu
HW3 Beras
Oct. 20, Tue
Machine Translation (Recording)
Chen Sun
Oct. 20, Tue
Final Final project proposal
Oct. 22, Thu
LSTM and Transformers
Chen Sun
Oct. 27, Tue
Large Language Models and Multimodal Learning
Chen Sun
Oct. 29, Thu
Final Project Idea Pitch
Oct. 29, Thu
HW4 Transformers and Min-Llama
Nov. 5, Thu
Generative Models for Robotic Learning
Zilai Zeng
Nov. 10, Tue
INVITED Invited Talk
Rosie Zhao
Nov. 12, Thu
Diffusion Models
Chen Sun
Nov. 12, Thu
MP1 Diffusion Models
Nov. 17, Tue
Deep Generative Models in Practice
Chen Sun
Nov. 19, Thu
INVITED Invited Talk
Mehul Damani
Nov. 24, Tue
Reinforcement Learning for LLMs
Zitian Tang
Nov. 24, Tue
MP2 Reinforcement Learning for LLMs
Dec. 1, Tue
INVITED Invited Talk
Jiatao Gu (tentative)
Dec. 3, Thu
INVITED Invited Talk
Tejas Kulkarni (tentative)
Dec. 8, Tue
Reinforcement Learning beyond LLMs
Chen Sun
Dec. 10, Thu
Final Project Presentations
Dec. 17, Thu
Final Final Project Deliverables Due